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Data Integration or Data Migration: A Comprehensive Comparative Study

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Data Integration or Data Migration

Learn the differences between data migration and data integration and how these two processes align with your company’s digital transformation strategy.

If you are invested in the world of data, at some point you surely heard the words data integration and data migration. They sound similar, but in reality they identify two very different processes, with different applications and use cases; it is easy to get confused, especially for those new to the latest technology development, but it is crucial to understand the differences. We have put together a comprehensive guide to understand the difference between data integration and data migration solutions, and to know when to employ each one.

Executives today are always looking at better and more efficient ways to collect, manage, store and analyze data. This is due to the increasing relevance data is gaining in the competitive scenario; it’s enough to know that Fortune Business Insights evaluated the global big data analytics market at $307.52 billion.

For everyone wanting to stay ahead of competition and not miss important developments, here is a guide on two processes which are gaining more and more attention: data integration and data migration.

Data integration and data migration: what are the differences?

If you are confused by data integration and data migration, you are not the only one. Even if they have similar names and are somehow related, these two processes are very different and are best applied on different use cases. They both identify a transfer of data, but method and goals are different.

Data integration is the process of unifing data from multiple sources into a single repository. Creating a unified set of information is extremely useful and efficient for both operational and analytical purposes, and for this reason data integration is one of the core elements of data management.

Data migration still involves moving data, but simply from one storage unit to another. It can be referred to both physical storage units, or clouds.

Let’s now have a look at the different purposes and use cases.

Differences in purpose

  • Data integration’s goal is to improve data organization, visibility and access.
  • Data migration’s purpose is to upgrade to a new system, usually for analytics or security reasons.

Differences in frequency

  • Data integration should be a continuous process in order to support the company’s daily activities.
  • Data migration is usually a one-time activity, occurring when a company is implementing a new system.

How to set up your data strategy

Data migration and data integration are both extremely useful processes, but companies need to know when to employ one or another, in order not to waste time and resources. According to your specific needs, which operation is better?

To summarize, data integration focuses on bringing together existing and new data from different sources, while data migration means transferring existing data to a new location.

Hevo has produced an accurate list of use cases for each process, to help executives understand better when to employ data migration services or invest in data integration. Here are the most common.

Data integration use cases:

Sources integration. For companies having multiple data sources, management and organization can be tricky, reducing data analytics capabilities. Integrating data from all sources makes reporting, analysis and decision-making simpler, quicker and more efficient.

Data quality.  Data integration can be used to cleanse data from error and duplicates, and find missing information. All this improves quality, accuracy, timing and consistency.

Data security. Dby allowing access control at the source, data integration boosts security.

Data migration use cases typically include:

System upgrading or replacing. Organizations can take advantage of new technologies by replacing a new system for a better one and moving all its existing data.

Implementing cloud systems. Data migration applies also in the case of migrating from on-premises systems to the cloud; data storage offers scalability, security and cost-efficiency very attractive to modern companies looking to grow.

Archiving data. Data no longer needed can be archived through data migration to preserve historical data while at the same time freeing space in the current storage units.

Merging. When two organizations merge, they may need to migrate their data to a single system in order to combine their operations.

Enabling data analytics. By migrating data to a system specifically designed for data analytics and business intelligence, companies can more easily access data and extract insights to make more informed decisions.

How to get started with data migration and data integration

Tredence offers state-of-the-art data migration solutions leveraging the power of AI. We will guide your company toward all the steps needed for a seamless, efficient data migration to adopt a scalable, agile and future-fit cloud platform. At Tredence, we provide guidance to employ data integration solutions and tools. Data cleansing, automation and integration will allow your company to have a faster architecture and higher-quality data, much needed for a company to thrive in today’s data-driven environment.

FAQs on Data Migration

What are the regulatory compliance considerations when it comes to Data Integration and Data Migration?

Data migration and data integration both require strict compliance to data protection laws like GDPR, HIPAA or CCPA. Especially during data migration, preventing breaches during transit is the main concern, while for data integration is ensuring privacy and consent.

What cybersecurity measures are essential during Data Integration and Data Migration processes?

Data encryption
Secure transfer protocol in place
Access control to all data at all times
Channel security
Essential data protection during data cleaning

What are the scalability options for Data Integration and Data Migration solutions?

Scalability offers many advantages in terms of cost efficiency and future-proof storage architecture. Data integration and data migration both involve transferring data to a more modern, efficient and secure storage unit. Learn more about scalability options on Tredence’s website.

Can you provide examples of industry leaders who have achieved competitive advantages through Data Integration and Data Migration?

An important rental company in the United States has integrated data into a comprehensive platform that offers seamless merging of data from various sources and 3d party data. The entire algorithm has been automated, increasing efficiency and reducing costs and risk. Download data migration eBook to learn how to accelerate migration by 50%.

What are the key performance indicators (KPIs) we should monitor during Data Integration and Data Migration projects?

Here are the main KPIs to track when embarking in data integration or data migration:
Data Accuracy
Migration downtime
Successful data transfers
Post-migration system performance
Customer satisfaction

How do Data Integration and Data Migration align with our digital transformation strategy?

Both these processes are central to digital transformation, a huge and comprehensive process fundamental to stay ahead of the game and drive growth. Migrating to cloud, or to a more efficient and scalable storage, or unifying all data into one single storage unit are all operations that will improve your company’s digitalization and increase data efficiency.

How do Data Integration and Data Migration impact our customer experience and satisfaction levels?

Having access at all times to integrated data allows to have a clearer picture of customers interactions, feedback and reviews. From this data is it possible to get important customers’ insights to improve customer experience.

What role does data quality play in the success of Data Integration and Data Migration projects?

Data quality is pivotal for the success of data integration and data migration: inaccurate data can lead to errors during the process, undermining efforts.

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